Volumetric Measures Research Articles

Single-subject analysis of regional brain volumetric measures can be strongly influenced by the method for head size adjustment

PurposeTotal intracranial volume (TIV) is often a nuisance covariate in MRI-based brain volumetry. This study compared two TIV adjustment methods with respect to their impact on z-scores in single subject analyses of regional brain volume estimates.MethodsBrain parenchyma, hippocampus, thalamus, and TIV were segmented in a normal database comprising 5059 T1w images. Regional volume estimates were adjusted for TIV using the residual method or the proportion method. Age was taken into account by regression with both methods. TIV- and age-adjusted regional volumes were transformed to z-scores and then compared between the two adjustment methods. Their impact on the detection of thalamus atrophy was tested in 127 patients with multiple sclerosis.ResultsThe residual method removed the association with TIV in all regions. The proportion method resulted in a switch of the direction without relevant change of the strength of the association. The reduction of physiological between-subject variability was larger with the residual method than with the proportion method. The difference between z-scores obtained with the residual method versus the proportion method was strongly correlated with TIV. It was larger than one z-score point in 5% of the subjects. The area under the ROC curve of the TIV- and age-adjusted thalamus volume for identification of multiple sclerosis patients was larger with the residual method than with the proportion method (0.84 versus 0.79).ConclusionThe residual method should be preferred for TIV and age adjustments of T1w-MRI-based brain volume estimates in single subject analyses.

Mathematical Abilities in School-Aged Children: A Structural Magnetic Resonance Imaging Analysis With Radiomics.

Structural magnetic resonance imaging (sMRI) studies have shown that children that differ in some mathematical abilities show differences in gray matter volume mainly in parietal and frontal regions that are involved in number processing, attentional control, and memory. In the present study, a structural neuroimaging analysis based on radiomics and machine learning models is presented with the aim of identifying the brain areas that better predict children’s performance in a variety of mathematical tests. A sample of 77 school-aged children from third to sixth grade were administered four mathematical tests: Math fluency, Calculation, Applied problems and Quantitative concepts as well as a structural brain imaging scan. By extracting radiomics related to the shape, intensity, and texture of specific brain areas, we observed that areas from the frontal, parietal, temporal, and occipital lobes, basal ganglia, and limbic system, were differentially related to children’s performance in the mathematical tests. sMRI-based analyses in the context of mathematical performance have been mainly focused on volumetric measures. However, the results for radiomics-based analysis showed that for these areas, texture features were the most important for the regression models, while volume accounted for less than 15% of the shape importance. These findings highlight the potential of radiomics for more in-depth analysis of medical images for the identification of brain areas related to mathematical abilities.

Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes

BackgroundIdentifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. MethodsHYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). ResultsThe optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. ConclusionsWe identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Advanced diffusion-weighted imaging models better characterize white matter neurodegeneration and clinical outcomes in multiple sclerosis.

White matter (WM) atrophy is relevant in multiple sclerosis (MS), but the methods of analysis currently used are not specific for microstructural changes. The aims of this study were to assess the use of advanced diffusion-weighted imaging (DWI) techniques proposed as measures of baseline and longitudinal WM atrophy in MS and to analyze whether these measures helped explain MS clinical disability (including cognitive impairment) better than volumetric and diffusion tensor (DT)-derived measures. 3DT1-weighted and DWI sequences were applied to 86 MS and 55 healthy controls (HC) at baseline and after one-year. Intra-cellular volume (vic) maps were computed from neurite orientation dispersion and density imaging model. Voxel-wise fiber-bundle cross-section (FCS) atrophy in MS compared to HC was estimated. Maps of fractional anisotropy and mean diffusivity were also obtained from DWI for a comparison with the proposed advanced DW-derived measures (vic and FCS). Both at baseline and after 1-year, only FCS measure showed a significant atrophy in relapsing-remitting (RR) MS compared to HC and in progressive MS compared to RRMS, mainly located in specific WM tracts (corticospinal tract, splenium of the corpus callosum, left optic radiation, bilateral cingulum, middle cerebellar peduncle and anterior commissure, p value < 0.05). Global baseline FCS and vic were the selected predictors of clinical (R-sq = 0.33, p = 0.007) and cognitive scores (R-sq = 0.29, p = 0.0014) in a linear regression model. Voxel-based FCS was able to detect WM tracts atrophy in MS clinical phenotypes with greater anatomical specificity compared to other measures (volumetric and DT-derived measures of WM damage). FCS and vic measured at baseline in the WM were the best predictors of clinical disability and cognitive impairment.

3D quantitative analysis of normal clitoral anatomy in nulliparous women by MRI.

We present a 3D computational approach for automated clitoral measurements. We hypothesized that computationally derived measurements would be comparable and less variable than reported manual measures. In this retrospective study, MRIs of 22 nulliparous women age 20-49 years with normal vaginal and clitoral anatomy were collected. Manual segmentations were performed to reconstruct 3D models of the whole clitoris (glans, body, crura, and bulbs) and vagina. The length, width, and volume of the clitoral structures and the distance between the vagina and clitoral structures were calculated. Computed clitoral morphometrics (length, width) were compared to median [range] values from a previously published cadaver study (N = 22) using the median test and Moses extreme reaction test. Calculated distances were compared to mean (± SD) reported by a 2D MRI study (N = 20) using independent t-test and Levene's test. Overall, computed clitoral morphometrics were similar to manual cadaver measurements, where the majority of length and width measures had ~1-2 mm difference and had less variability (smaller range). All calculated distances were significantly smaller and had smaller SDs than manual 2D MRI values, with two-fold differences in the means and SDs. Large variation was observed in clitoral volumetric measures in our cohort. The proposed 3D computational method improves the standardization and consistency of clitoral measurements compared to traditional manual approaches. The use of this approach in radiographic studies will give better insight into how clitoral anatomy relates to sexual function and how both are impacted by gynecologic surgery, where outcomes can assist treatment planning.

Fairness in Cardiac Magnetic Resonance Imaging: Assessing Sex and Racial Bias in Deep Learning-Based Segmentation.

BackgroundArtificial intelligence (AI) techniques have been proposed for automation of cine CMR segmentation for functional quantification. However, in other applications AI models have been shown to have potential for sex and/or racial bias. The objective of this paper is to perform the first analysis of sex/racial bias in AI-based cine CMR segmentation using a large-scale database.MethodsA state-of-the-art deep learning (DL) model was used for automatic segmentation of both ventricles and the myocardium from cine short-axis CMR. The dataset consisted of end-diastole and end-systole short-axis cine CMR images of 5,903 subjects from the UK Biobank database (61.5 ± 7.1 years, 52% male, 81% white). To assess sex and racial bias, we compared Dice scores and errors in measurements of biventricular volumes and function between patients grouped by race and sex. To investigate whether segmentation bias could be explained by potential confounders, a multivariate linear regression and ANCOVA were performed.ResultsResults on the overall population showed an excellent agreement between the manual and automatic segmentations. We found statistically significant differences in Dice scores between races (white ∼94% vs. minority ethnic groups 86–89%) as well as in absolute/relative errors in volumetric and functional measures, showing that the AI model was biased against minority racial groups, even after correction for possible confounders. The results of a multivariate linear regression analysis showed that no covariate could explain the Dice score bias between racial groups. However, for the Mixed and Black race groups, sex showed a weak positive association with the Dice score. The results of an ANCOVA analysis showed that race was the main factor that can explain the overall difference in Dice scores between racial groups.ConclusionWe have shown that racial bias can exist in DL-based cine CMR segmentation models when training with a database that is sex-balanced but not race-balanced such as the UK Biobank.

Total Brain Volumetric Measures and Schizophrenia Risk: A Two-Sample Mendelian Randomization Study.

Schizophrenia (SCZ) is an idiopathic psychiatric disorder with a heritable component and a substantial public health impact. Although abnormalities in total brain volumetric measures (TBVMs) have been found in patients with SCZ, it is still unknown whether these abnormalities have a causal effect on the risk of SCZ. Here, we performed a Mendelian randomization (MR) study to investigate the possible causal associations between each TBVM and SCZ risk. Specifically, genome-wide association study (GWAS) summary statistics of total gray matter volume, total white matter volume, total cerebrospinal fluid volume, and total brain volume were obtained from the United Kingdom Biobank database (33,224 individuals), and SCZ GWAS summary statistics were provided by the Psychiatric Genomics Consortium (150,064 individuals). The main MR analysis was conducted using the inverse variance weighted method, and other MR methods, including MR-Egger, weighted median, simple mode, and weighted mode methods, were performed to assess the robustness of our findings. For pleiotropy analysis, we employed three approaches: MR-Egger intercept, MR-PRESSO, and heterogeneity tests. No TBVM was causally associated with SCZ risk according to the MR results, and no significant pleiotropy or heterogeneity was found for instrumental variables. Taken together, this study suggested that alterations in TBVMs were not causally associated with the risk of SCZ.

Large-scale societal dynamics are reflected in human mood and brain

The stock market is a bellwether of socio-economic changes that may directly affect individual well-being. Using large-scale UK-biobank data generated over 14 years, we applied specification curve analysis to rigorously identify significant associations between the local stock market index (FTSE100) and 479,791 UK residents’ mood, as well as their alcohol intake and blood pressure adjusting the results for a large number of potential confounders, including age, sex, linear and non-linear effects of time, research site, other stock market indexes. Furthermore, we found similar associations between FTSE100 and volumetric measures of affective brain regions in a subsample (n = 39,755; measurements performed over 5.5 years), which were particularly strong around phase transitions characterized by maximum volatility in the market. The main findings did not depend on applied effect-size estimation criteria (linear methods or mutual information criterion) and were replicated in two independent US-based studies (Parkinson’s Progression Markers Initiative; n = 424; performed over 2.5 years and MyConnectome; n = 1; 81 measurements over 1.5 years). Our results suggest that phase transitions in the society, indexed by stock market, exhibit close relationships with human mood, health and the affective brain from an individual to population level.

Serum NfL in Alzheimer Dementia: Results of the Prospective Dementia Registry Austria.

Background and Objectives: The neurofilament light chain (NfL) is a biomarker for neuro-axonal injury in various acute and chronic neurological disorders, including Alzheimer’s disease (AD). We here investigated the cross-sectional and longitudinal associations between baseline serum NfL (sNfL) levels and cognitive, behavioural as well as MR volumetric findings in the Prospective Dementia Registry Austria (PRODEM-Austria). Materials and Methods: All participants were clinically diagnosed with AD according to NINCDS-ADRDA criteria and underwent a detailed clinical assessment, cognitive testing (including the Mini Mental State Examination (MMSE) and the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD)), the neuropsychiatric inventory (NPI) and laboratory evaluation. A total of 237 patients were included in the study. Follow-up examinations were done at 6 months, 1 year and 2 years with 93.3% of patients undergoing at least one follow-up. We quantified sNfL by a single molecule array (Simoa). In a subgroup of 125 subjects, brain imaging data (1.5 or 3T MRI, with 1 mm isotropic resolution) were available. Brain volumetry was assessed using the FreeSurfer image analysis suite (v6.0). Results: Higher sNfL concentrations were associated with worse performance in cognitive tests at baseline, including CERAD (B = −10.084, SE = 2.999, p < 0.001) and MMSE (B = −3.014, SE = 1.293, p = 0.021). The sNfL levels also correlated with the presence of neuropsychiatric symptoms (NPI total score: r = 0.138, p = 0.041) and with smaller volumes of the temporal lobe (B = −0.012, SE = 0.003, p = 0.001), the hippocampus (B = −0.001, SE = 0.000201, p = 0.013), the entorhinal (B = −0.000308, SE = 0.000124, p = 0.014), and the parahippocampal cortex (B = −0.000316, SE = 0.000113, p = 0.006). The sNfL values predicted more pronounced cognitive decline over the mean follow-up period of 22 months, but there were no significant associations with respect to change in neuropsychiatric symptoms and brain volumetric measures. Conclusions: the sNfL levels relate to cognitive, behavioural, and imaging hallmarks of AD and predicts short term cognitive decline.

Together Intra-Tumor Hypoxia and Macrophagic Immunity Are Driven Worst Outcome in Pediatric High-Grade Osteosarcomas.

Simple SummaryRadiological and immunohistochemical data were correlated with the outcome in a retrospective monocentric cohort of 30 pediatric osteosarcomas (OTS). A necrotic volume of more than 50 cm3 at diagnosis was significantly linked to a worse overall survival (OS). Regarding immunohistochemical analyses, an overexpression of hypoxic markers, such as HIF-1α and anhydrase carbonic IX (CAIX), was significantly linked to a worse OS, while pS6-RP hyperexpression was correlated with a better survival. We also featured that CD68 positive cells, representative of macrophagic M1 polarization, were mostly associated with HIF-1α and CAIX hyperexpressions and that M2-like polarization, mostly related to CD163 positivity, was correlated to mTor activation. These findings, involving clinical, radiological and biology data, allowed us to hypothesize a dual signature association ready to use routinely in future protocols.Background: Osteosarcomas (OTS) represent the most common primary bone cancer diagnosed in adolescents and young adults. Despite remarkable advances, there are no objective molecular or imaging markers able to predict an OTS outcome at diagnosis. Focusing on biomarkers contributing broadly to treatment resistance, we examine the interplay between the tumor-associated macrophages and intra-tumor hypoxia. Methods: Radiological and immunohistochemical (IHC) data were correlated with the outcome in a retrospective and monocentric cohort of 30 pediatric OTS. We studied hypoxic (pS6, phospho-mTor, HIF-1α and carbonic anhydrase IX (CAIX)) and macrophagic (CD68 and CD163) biomarkers. Results: The imaging analyses were based on MRI manual volumetric measures on axial post-contrast T1 weighted images, where, for each tumor, we determined the necrotic volume and its ratio to the entire tumor volume. When they were above 50 cm3 and 20%, respectively, they correlated with a worse overall survival (p = 0.0072 and p = 0.0136, respectively) and event-free survival (p = 0.0059 and p = 0.0143, respectively). IHC assessments enable a significant statistical link between HIF-1α/CAIX hyper-expressions, CD68+ cells and a worse outcome, whereas activation of mTor pathway was linked to a better survival rate and CD163+ cells. Conclusions: This study evidenced the links between hypoxia and immunity in OTS, as their poor outcome may be related to a larger necrotic volume on diagnostic MRI and, in biopsies, to a specific IHC profile.

Short Juvenile Stress Has No Long-Lasting Effects on Anxiety-Like Behavior, Object Recognition Memory, or Gross Brain Morphology but Affects Dendritic Spines in the Hippocampus in Male Rats

Purpose: During the juvenile stage, such areas as the hippocampus and corpus callosum (CC) are still immature and sensitive to stress exposure. The present study investigated whether two different types of stressors in the juvenile stage of life have a long-lasting impact on behavior and biological outcomes in adult rats. Methods: Male juvenile rats were exposed to restraint or predator stress on postnatal day 25 (P25) for 3 days. Thirty-two days later (P60-74), behavioral and biological analyses were conducted. The behavioral analysis included measures of anxiety-like behavior and recognition memory. The biological analysis investigated gross cerebral morphology, based on volume analysis of the CC and hippocampus, perirhinal cortex thickness, and dendritic spine density. Results: Neither restraint stress nor predator stress affected anxiety-like behavior or object recognition memory in adulthood. Body weight and adrenal gland weight were unaffected by both types of stress. Overall, volumetric measures of the CC and hippocampus were not significant, with no changes in perirhinal cortex thickness. Spine density in the medial prefrontal cortex also was unaffected, but a decrease in dendritic spine density was found in the hippocampus in response to restraint stress and an increase to predator stress. Conclusion: Short-term and daily restraint and predator stress during the juvenile stage had no long-lasting effects on anxiety-like behavior, object memory, volume of the CC or hippocampus, or perirhinal cortex thickness, but a decrease in dendritic spine density was found in the hippocampus. These findings suggest that different types of stressors have different impacts on microstructures in the brain without affecting behavior or the gross morphology of stress-sensitive brain areas.

Comparison of breast density assessments between synthesized C-View™ & intelligent 2D™ mammography.

To compare breast density assessments between C-View™ and Intelligent 2D™, different generations of synthesized mammography (SM) from Hologic. In this retrospective study, we identified a subset of females between March 2017 and December 2019 who underwent screening digital breast tomosynthesis (DBT) with C-View followed by DBT with Intelligent 2D. Clinical Breast Imaging Reporting and Database System breast density was obtained along with volumetric breast density measures (including density grade, breast volume, percentage volumetric density, dense volume) using VolparaTM. Differences in density measures by type of synthesized image were calculated using the pairwise t-test or McNemar's test, as appropriate. 67 patients (avg age 62.7; range 40-84) were included with an average of 13.3 months between the two exams. No difference was found in Breast Imaging Reporting and Database System density between the SM reconstructions (p = 0.74). Similarly, there was no difference in VolparaTM mean density grade (p = 0.71), mean breast volume (p = 0.48), mean dense volume (p = 0.43) or mean percent volumetric density (p = 0.12) between the exams. We found no significant differences in clinical and automated breast density assessments between these two versions of SM. Lack of differences in density estimates between the two SM reconstructions is important as density assignment impacts risk stratification and adjunct screening recommendations.

Zika virus congenital microcephaly severity classification and the association of severity with neuropsychomotor development.

Zika virus infection during pregnancy is linked to birth defects, most notably microcephaly, which is associated with neurodevelopmental delays. The goals of the study were to propose a method for severity classification of congenital microcephaly based on neuroradiologic findings of MRI scans, and to investigate the association of severity with neuropsychomotor developmental scores. We also propose a semi-automated method for MRI-based severity classification of microcephaly. We conducted a cross-sectional investigation of 42 infants born with congenital Zika infection. Bayley Scales of Infant and Toddler Development III (Bayley-III) developmental evaluations and MRI scans were carried out at ages 13-39months (mean: 24.8months; standard deviation [SD]: 5.8months). The severity score was generated based on neuroradiologist evaluations of brain malformations. Next, we established a distribution of Zika virus-microcephaly severity score including mild, moderate and severe and investigated the association of severity with neuropsychomotor developmental scores. Finally, we propose a simplified semi-automated procedure for estimating the severity score based only on volumetric measures. The results showed a correlation of r=0.89 (P<0.001) between the Zika virus-microcephaly severity score and the semi-automated method. The trimester of infection did not correlate with the semi-automated method. Neuropsychomotor development correlated with the severity classification based on the radiologic readings and semi-automated method; the more severe the imaging scores, the lower the neuropsychomotor developmental scores. These severity classification methods can be used to evaluate severity of microcephaly and possible association with developmental consequences. The semi-automated methods thus provide an alternative for predicting severity of microcephaly based on only one MRI sequence.

Computed Tomography and Magnetic Resonance Correlation and Agreement for Foot Muscle and Adipose Tissue Measurements.

Volumetric measures of intrinsic foot muscle and intermuscular adipose tissue (IMAT) infiltration are important in understanding foot injury and disease. We questioned whether measures of muscle and fat derived from computed tomography (CT) and magnetic resonance (MR) would be comparable. This study determined the correlation and level of agreement between CT and MR measurements of foot muscle and IMAT from 32 subjects with diabetes and peripheral neuropathy. Volumetric CT and DIXON 3T MR scans were obtained. Intermuscular adipose tissue and muscle volumes used to create the IMAT to muscle ratio were obtained by segmenting the forefoot muscle compartment from each modality. Computed tomography IMAT ratios were significantly correlated (r = 0.85, P < 0.0001) with MR IMAT ratios. Computed tomography and MR IMAT ratio mean difference between CT and MR was small (0.044 unit, Bland-Altman plots). The CT and MR IMAT ratio measurements were highly correlated, indicating both modalities represent tissue quantification similarly.Level of Evidence: 2Technical Efficacy: 1.

Associations of Stages of Objective Memory Impairment With Amyloid PET and Structural MRI: The A4 Study.

Background and ObjectivesThe goal of this work was to investigate the neuroimaging correlates of the Stages of Objective Memory Impairment (SOMI) system operationalized with the Free and Cued Selective Reminding Test (FCSRT), a widely used episodic memory measure.MethodsThe FCSRT begins with a study phase in which items (e.g., grapes) are identified in response to unique semantic cues (e.g., fruit) that are used in the test phase to prompt recall of items not retrieved by free recall. There are 3 test trials of the 16 items (maximum 48). Data from 4,484 cognitively unimpaired participants from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study were used. All participants had amyloid PET imaging, and a subset of 1,262 β-amyloid (Aβ)–positive had structural MRIs. We compared the Aβ mean cortical standardized uptake value ratio (SUVR) and volumetric measures of hippocampus, parahippocampal gyrus, entorhinal cortex, and inferior temporal cortex between the 5 SOMI stages.ResultsParticipants had a mean age of 71.3 (SD 4.6) years; 40.6% were male; and 34.6% were APOE ε4 positive. Half had no memory impairment; the other half had retrieval deficits, storage limitations, or both. Analysis of covariance in the entire sample while controlling for age, sex, education, and APOE ε4 showed that individuals in higher SOMI stages had higher global amyloid SUVR (p < 0.001). Both SOMI-4 and -3 subgroups had higher amyloid SUVR than SOMI-0 and SOMI-1 subgroups. Individuals in higher SOMI stages had smaller hippocampal volume (p = 0.003), entorhinal cortex (p < 0.05), and inferior temporal lobes (p < 0.05), but there was no difference between parahippocampal gyrus volume of different SOMI stages. Pairwise comparison of SOMI subgroups showed that the SOMI-4, -3, and -2 subgroups had smaller hippocampal volume than the SOMI-0 and -1 subgroup. The SOMI-4 subgroup had significantly smaller entorhinal cortex and smaller inferior temporal lobe compared to all other groups.DiscussionPresence of Alzheimer disease pathology is closely related to memory impairment according to SOMI stages in the cognitively unimpaired sample of A4. Results from structural MRIs suggest that memory storage impairment (SOMI-3 and -4) is present when there is widespread medial temporal lobe atrophy.Trial Registration InformationClinicalTrials.gov identifier: NCT02008357.Classification of EvidenceThis study provides Class I evidence that, in normal older individuals, higher stages of memory impairment assessed with FCSRT were associated with higher amyloid imaging burden and lower volume of hippocampus, entorhinal cortex, and inferior temporal lobes.

Two neuroanatomical subtypes of males with autism spectrum disorder revealed using semi-supervised machine learning

BackgroundClinical and etiological varieties remain major obstacles to decompose heterogeneity in autism spectrum disorders (ASD). Recently, neuroimaging raised new hope to identify neurosubtypes of ASD for further understanding the biological mechanisms behind the disorder.MethodsIn this study, brain structural MRI data and clinical measures of 221 male subjects with ASD and 257 healthy controls were selected from 7 independent sites from the Autism Brain Image Data Exchange database (ABIDE). Heterogeneity through discriminative analysis (HYDRA), a recently-proposed semi-supervised clustering method was utilized to divide individuals with ASD into several neurosubtypes by regional volumetric measures of gray matter, white matter, and cerebrospinal fluid. Voxel-wise volume, clinical measures, dynamic resting-state functional magnetic resonance imaging (R-fMRI) measures among different neurosubtypes of ASD were explored. In addition, support vector machine (SVM) model was applied to test whether the neurosubtyping of ASD could improve diagnostic accuracy of ASD.ResultsTwo neurosubtypes of ASD with different voxel-wise volumetric patterns were revealed. The full-scale intelligence quotient (IQ), verbal IQ, Autism Diagnostic Observation Schedule (ADOS) total scores and ADOS severity scores were significantly different between the two neurosubtypes, the total intracranial volume was correlated with performance IQ in Subtype 1 and was correlated with ADOS communication score and ADOS social score in Subtype 2. Compared with Subtype 2, Subtype 1 showed lower dynamic R-fMRI measures, lower dynamic functional architecture stability, higher mean and lower standard deviation (SD) of concordance among dynamic R-fMRI measures in cerebellum. In addition, classification accuracies between ASD neurosubtypes and healthy controls were significantly improved compared with classification accuracy between entire ASD group and healthy controls.LimitationsThe present study excluded female subjects and left-handed subjects, which limited the ability to investigate the associations between these factors and the heterogeneity of ASD.ConclusionsThe two distinct neuroanatomical subtypes of ASD validated by other data modalities not only adds reliability of the result, but also bridges from brain phenomenology to clinical behavior. The current neurosubtypes of ASD could facilitate understanding the neuropathology of this disorder and could be potentially used to improve clinical decision-making process and optimize treatment.

Reliability and agreement of lumbar multifidus volume and fat fraction quantification using magnetic resonance imaging

IntroductionMagnetic resonance imaging (MRI) is the standard to quantify size and structure of lumbar muscles. Three-dimensional volumetric measures are expected to be more closely related to muscle function than two-dimensional measures such as cross-sectional area. Reliability and agreement of a standardized method should be established to enable the use of MRI to assess lumbar muscle characteristics. ObjectivesThis study investigates the intra- and inter-processor reliability for the quantification of (1) muscle volume and (2) fat fraction based on chemical shift MRI images using axial 3D-volume measurements of the lumbar multifidus in patients with low back pain. MethodsTwo processors manually segmented the lumbar multifidus on the MRI scans of 18 patients with low back pain using Mevislab software following a well-defined method. Fat fraction of the segmented volume was calculated. Reliability and agreement were determined using intra-class correlation coefficients (ICC), Bland-Altman plots and calculation of the standard error of measurement (SEM) and minimal detectable change (MDC). ResultsExcellent ICCs were found for both intra-processor and inter-processor analysis of lumbar multifidus volume measurement, with slightly better results for the intra-processor reliability. The SEMs for volume were lower than 4.1 cm³. Excellent reliability and agreement were also found for fat fraction measures, with ICCs of 0.985–0.998 and SEMs below 0.946%. ConclusionThe proposed method to quantify muscle volume and fat fraction of the lumbar multifidus on MRI was highly reliable, and can be used in further research on lumbar multifidus structure.

Retinal layer assessments as potential biomarkers for brain atrophy in the Rhineland Study

Retinal assessments have been discussed as biomarkers for brain atrophy. However, available studies did not investigate all retinal layers due to older technology, reported inconsistent results, or were based on small sample sizes. We included 2872 eligible participants of the Rhineland Study with data on spectral domain–optical coherence tomography (SD–OCT) and brain magnetic resonance imaging (MRI). We used multiple linear regression to examine relationships between retinal measurements and volumetric brain measures as well as fractional anisotropy (FA) as measure of microstructural integrity of white matter (WM) for different brain regions. Mean (SD) age was 53.8 ± 13.2 years (range 30–94) and 57% were women. Volumes of the inner retina were associated with total brain and grey matter (GM) volume, and even stronger with WM volume and FA. In contrast, the outer retina was mainly associated with GM volume, while both, inner and outer retina, were associated with hippocampus volume. While we extend previously reported associations between the inner retina and brain measures, we found additional associations of the outer retina with parts of the brain. This indicates that easily accessible retinal SD-OCT assessments may serve as biomarkers for clinical monitoring of neurodegenerative diseases and merit further research.

The neurobiological characterization of distinct cognitive subtypes in early-phase schizophrenia-spectrum disorders

IntroductionCognitive deficits are present in some, but not all patients with schizophrenia-spectrum disorders (SSD). We and others have demonstrated three cognitive clusters: cognitively intact patients, patients with deficits in a few domains and those with global cognitive deficits. This study aimed to identify cognitive subtypes of early-phase SSD with matched controls as a reference group, and evaluated cognitive subgroups regarding clinical and brain volumetric measures. MethodsEighty-six early-phase SSD patients were included. Hierarchical cluster analysis was conducted using global performance on the Brief Assessment of Cognition in Schizophrenia (BACS). Cognitive subgroups were subsequently related to clinical and brain volumetric measures (cortical, subcortical and cortical thickness) using ANCOVA. ResultsThree distinct cognitive clusters emerged: relative to controls we found one cluster of patients with preserved cognition (n = 25), one moderately impaired cluster (n = 38) and one severely impaired cluster (n = 23). Cognitive subgroups were characterized by differences in volume of the left postcentral gyrus, left middle caudal frontal gyrus and left insula, while differences in cortical thickness were predominantly found in fronto-parietal regions. No differences were demonstrated in subcortical brain volume. DiscussionCurrent results replicate the existence of three distinct cognitive subgroups including one relatively large group with preserved cognitive function. Cognitive subgroups were characterized by differences in cortical regional brain volume and cortical thickness, suggesting associations with cortical, but not subcortical development and cognitive functioning such as attention, executive functions and speed of processing.

Qualitative and Quantitative Comparison of Hippocampal Volumetric Software Applications: Do All Roads Lead to Rome?

Brain volumetric software is increasingly suggested for clinical routine. The present study quantifies the agreement across different software applications. Ten cases with and ten gender- and age-adjusted healthy controls without hippocampal atrophy (median age: 70; 25–75% range: 64–77 years and 74; 66–78 years) were retrospectively selected from a previously published cohort of Alzheimer’s dementia patients and normal ageing controls. Hippocampal volumes were computed based on 3 Tesla T1-MPRAGE-sequences with FreeSurfer (FS), Statistical-Parametric-Mapping (SPM; Neuromorphometrics and Hammers atlases), Geodesic-Information-Flows (GIF), Similarity-and-Truth-Estimation-for-Propagated-Segmentations (STEPS), and Quantib™. MTA (medial temporal lobe atrophy) scores were manually rated. Volumetric measures of each individual were compared against the mean of all applications with intraclass correlation coefficients (ICC) and Bland–Altman plots. Comparing against the mean of all methods, moderate to low agreement was present considering categorization of hippocampal volumes into quartiles. ICCs ranged noticeably between applications (left hippocampus (LH): from 0.42 (STEPS) to 0.88 (FS); right hippocampus (RH): from 0.36 (Quantib™) to 0.86 (FS). Mean differences between individual methods and the mean of all methods [mm3] were considerable (LH: FS −209, SPM-Neuromorphometrics −820; SPM-Hammers −1474; Quantib™ −680; GIF 891; STEPS 2218; RH: FS −232, SPM-Neuromorphometrics −745; SPM-Hammers −1547; Quantib™ −723; GIF 982; STEPS 2188). In this clinically relevant sample size with large spread in data ranging from normal aging to severe atrophy, hippocampal volumes derived by well-accepted applications were quantitatively different. Thus, interchangeable use is not recommended.